// Copyright (c) 2012 The Chromium Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

#include "media/base/vector_math.h"
#include "media/base/vector_math_testing.h"

#include <algorithm>

#include "base/logging.h"
#include "build/build_config.h"

#if defined(WIN32)
#undef max
#undef min
#endif

// NaCl does not allow intrinsics.
#if defined(ARCH_CPU_X86_FAMILY) && !defined(OS_NACL)
#include <xmmintrin.h>
// Don't use custom SSE versions where the auto-vectorized C version performs
// better, which is anywhere clang is used.
#if !defined(__clang__)
#define FMAC_FUNC FMAC_SSE
#define FMUL_FUNC FMUL_SSE
#else
#define FMAC_FUNC FMAC_C
#define FMUL_FUNC FMUL_C
#endif
#define EWMAAndMaxPower_FUNC EWMAAndMaxPower_SSE
#elif defined(ARCH_CPU_ARM_FAMILY) && defined(USE_NEON)
#include <arm_neon.h>
#define FMAC_FUNC FMAC_NEON
#define FMUL_FUNC FMUL_NEON
#define EWMAAndMaxPower_FUNC EWMAAndMaxPower_NEON
#else
#define FMAC_FUNC FMAC_C
#define FMUL_FUNC FMUL_C
#define EWMAAndMaxPower_FUNC EWMAAndMaxPower_C
#endif

namespace media {
namespace vector_math {

    void FMAC(const float src[], float scale, int len, float dest[])
    {
        // Ensure |src| and |dest| are 16-byte aligned.
        DCHECK_EQ(0u, reinterpret_cast<uintptr_t>(src) & (kRequiredAlignment - 1));
        DCHECK_EQ(0u, reinterpret_cast<uintptr_t>(dest) & (kRequiredAlignment - 1));
        return FMAC_FUNC(src, scale, len, dest);
    }

    void FMAC_C(const float src[], float scale, int len, float dest[])
    {
        for (int i = 0; i < len; ++i)
            dest[i] += src[i] * scale;
    }

    void FMUL(const float src[], float scale, int len, float dest[])
    {
        // Ensure |src| and |dest| are 16-byte aligned.
        DCHECK_EQ(0u, reinterpret_cast<uintptr_t>(src) & (kRequiredAlignment - 1));
        DCHECK_EQ(0u, reinterpret_cast<uintptr_t>(dest) & (kRequiredAlignment - 1));
        return FMUL_FUNC(src, scale, len, dest);
    }

    void FMUL_C(const float src[], float scale, int len, float dest[])
    {
        for (int i = 0; i < len; ++i)
            dest[i] = src[i] * scale;
    }

    void Crossfade(const float src[], int len, float dest[])
    {
        float cf_ratio = 0;
        const float cf_increment = 1.0f / len;
        for (int i = 0; i < len; ++i, cf_ratio += cf_increment)
            dest[i] = (1.0f - cf_ratio) * src[i] + cf_ratio * dest[i];
    }

    std::pair<float, float> EWMAAndMaxPower(
        float initial_value, const float src[], int len, float smoothing_factor)
    {
        // Ensure |src| is 16-byte aligned.
        DCHECK_EQ(0u, reinterpret_cast<uintptr_t>(src) & (kRequiredAlignment - 1));
        return EWMAAndMaxPower_FUNC(initial_value, src, len, smoothing_factor);
    }

    std::pair<float, float> EWMAAndMaxPower_C(
        float initial_value, const float src[], int len, float smoothing_factor)
    {
        std::pair<float, float> result(initial_value, 0.0f);
        const float weight_prev = 1.0f - smoothing_factor;
        for (int i = 0; i < len; ++i) {
            result.first *= weight_prev;
            const float sample = src[i];
            const float sample_squared = sample * sample;
            result.first += sample_squared * smoothing_factor;
            result.second = std::max(result.second, sample_squared);
        }
        return result;
    }

#if defined(ARCH_CPU_X86_FAMILY) && !defined(OS_NACL)
    void FMUL_SSE(const float src[], float scale, int len, float dest[])
    {
        const int rem = len % 4;
        const int last_index = len - rem;
        __m128 m_scale = _mm_set_ps1(scale);
        for (int i = 0; i < last_index; i += 4)
            _mm_store_ps(dest + i, _mm_mul_ps(_mm_load_ps(src + i), m_scale));

        // Handle any remaining values that wouldn't fit in an SSE pass.
        for (int i = last_index; i < len; ++i)
            dest[i] = src[i] * scale;
    }

    void FMAC_SSE(const float src[], float scale, int len, float dest[])
    {
        const int rem = len % 4;
        const int last_index = len - rem;
        __m128 m_scale = _mm_set_ps1(scale);
        for (int i = 0; i < last_index; i += 4) {
            _mm_store_ps(dest + i, _mm_add_ps(_mm_load_ps(dest + i), _mm_mul_ps(_mm_load_ps(src + i), m_scale)));
        }

        // Handle any remaining values that wouldn't fit in an SSE pass.
        for (int i = last_index; i < len; ++i)
            dest[i] += src[i] * scale;
    }

// Convenience macro to extract float 0 through 3 from the vector |a|.  This is
// needed because compilers other than clang don't support access via
// operator[]().
#define EXTRACT_FLOAT(a, i) \
    (i == 0 ? _mm_cvtss_f32(a) : _mm_cvtss_f32(_mm_shuffle_ps(a, a, i)))

    std::pair<float, float> EWMAAndMaxPower_SSE(
        float initial_value, const float src[], int len, float smoothing_factor)
    {
        // When the recurrence is unrolled, we see that we can split it into 4
        // separate lanes of evaluation:
        //
        // y[n] = a(S[n]^2) + (1-a)(y[n-1])
        //      = a(S[n]^2) + (1-a)^1(aS[n-1]^2) + (1-a)^2(aS[n-2]^2) + ...
        //      = z[n] + (1-a)^1(z[n-1]) + (1-a)^2(z[n-2]) + (1-a)^3(z[n-3])
        //
        // where z[n] = a(S[n]^2) + (1-a)^4(z[n-4]) + (1-a)^8(z[n-8]) + ...
        //
        // Thus, the strategy here is to compute z[n], z[n-1], z[n-2], and z[n-3] in
        // each of the 4 lanes, and then combine them to give y[n].

        const int rem = len % 4;
        const int last_index = len - rem;

        const __m128 smoothing_factor_x4 = _mm_set_ps1(smoothing_factor);
        const float weight_prev = 1.0f - smoothing_factor;
        const __m128 weight_prev_x4 = _mm_set_ps1(weight_prev);
        const __m128 weight_prev_squared_x4 = _mm_mul_ps(weight_prev_x4, weight_prev_x4);
        const __m128 weight_prev_4th_x4 = _mm_mul_ps(weight_prev_squared_x4, weight_prev_squared_x4);

        // Compute z[n], z[n-1], z[n-2], and z[n-3] in parallel in lanes 3, 2, 1 and
        // 0, respectively.
        __m128 max_x4 = _mm_setzero_ps();
        __m128 ewma_x4 = _mm_setr_ps(0.0f, 0.0f, 0.0f, initial_value);
        int i;
        for (i = 0; i < last_index; i += 4) {
            ewma_x4 = _mm_mul_ps(ewma_x4, weight_prev_4th_x4);
            const __m128 sample_x4 = _mm_load_ps(src + i);
            const __m128 sample_squared_x4 = _mm_mul_ps(sample_x4, sample_x4);
            max_x4 = _mm_max_ps(max_x4, sample_squared_x4);
            // Note: The compiler optimizes this to a single multiply-and-accumulate
            // instruction:
            ewma_x4 = _mm_add_ps(ewma_x4,
                _mm_mul_ps(sample_squared_x4, smoothing_factor_x4));
        }

        // y[n] = z[n] + (1-a)^1(z[n-1]) + (1-a)^2(z[n-2]) + (1-a)^3(z[n-3])
        float ewma = EXTRACT_FLOAT(ewma_x4, 3);
        ewma_x4 = _mm_mul_ps(ewma_x4, weight_prev_x4);
        ewma += EXTRACT_FLOAT(ewma_x4, 2);
        ewma_x4 = _mm_mul_ps(ewma_x4, weight_prev_x4);
        ewma += EXTRACT_FLOAT(ewma_x4, 1);
        ewma_x4 = _mm_mul_ss(ewma_x4, weight_prev_x4);
        ewma += EXTRACT_FLOAT(ewma_x4, 0);

        // Fold the maximums together to get the overall maximum.
        max_x4 = _mm_max_ps(max_x4,
            _mm_shuffle_ps(max_x4, max_x4, _MM_SHUFFLE(3, 3, 1, 1)));
        max_x4 = _mm_max_ss(max_x4, _mm_shuffle_ps(max_x4, max_x4, 2));

        std::pair<float, float> result(ewma, EXTRACT_FLOAT(max_x4, 0));

        // Handle remaining values at the end of |src|.
        for (; i < len; ++i) {
            result.first *= weight_prev;
            const float sample = src[i];
            const float sample_squared = sample * sample;
            result.first += sample_squared * smoothing_factor;
            result.second = std::max(result.second, sample_squared);
        }

        return result;
    }
#endif

#if defined(ARCH_CPU_ARM_FAMILY) && defined(USE_NEON)
    void FMAC_NEON(const float src[], float scale, int len, float dest[])
    {
        const int rem = len % 4;
        const int last_index = len - rem;
        float32x4_t m_scale = vmovq_n_f32(scale);
        for (int i = 0; i < last_index; i += 4) {
            vst1q_f32(dest + i, vmlaq_f32(vld1q_f32(dest + i), vld1q_f32(src + i), m_scale));
        }

        // Handle any remaining values that wouldn't fit in an NEON pass.
        for (int i = last_index; i < len; ++i)
            dest[i] += src[i] * scale;
    }

    void FMUL_NEON(const float src[], float scale, int len, float dest[])
    {
        const int rem = len % 4;
        const int last_index = len - rem;
        float32x4_t m_scale = vmovq_n_f32(scale);
        for (int i = 0; i < last_index; i += 4)
            vst1q_f32(dest + i, vmulq_f32(vld1q_f32(src + i), m_scale));

        // Handle any remaining values that wouldn't fit in an NEON pass.
        for (int i = last_index; i < len; ++i)
            dest[i] = src[i] * scale;
    }

    std::pair<float, float> EWMAAndMaxPower_NEON(
        float initial_value, const float src[], int len, float smoothing_factor)
    {
        // When the recurrence is unrolled, we see that we can split it into 4
        // separate lanes of evaluation:
        //
        // y[n] = a(S[n]^2) + (1-a)(y[n-1])
        //      = a(S[n]^2) + (1-a)^1(aS[n-1]^2) + (1-a)^2(aS[n-2]^2) + ...
        //      = z[n] + (1-a)^1(z[n-1]) + (1-a)^2(z[n-2]) + (1-a)^3(z[n-3])
        //
        // where z[n] = a(S[n]^2) + (1-a)^4(z[n-4]) + (1-a)^8(z[n-8]) + ...
        //
        // Thus, the strategy here is to compute z[n], z[n-1], z[n-2], and z[n-3] in
        // each of the 4 lanes, and then combine them to give y[n].

        const int rem = len % 4;
        const int last_index = len - rem;

        const float32x4_t smoothing_factor_x4 = vdupq_n_f32(smoothing_factor);
        const float weight_prev = 1.0f - smoothing_factor;
        const float32x4_t weight_prev_x4 = vdupq_n_f32(weight_prev);
        const float32x4_t weight_prev_squared_x4 = vmulq_f32(weight_prev_x4, weight_prev_x4);
        const float32x4_t weight_prev_4th_x4 = vmulq_f32(weight_prev_squared_x4, weight_prev_squared_x4);

        // Compute z[n], z[n-1], z[n-2], and z[n-3] in parallel in lanes 3, 2, 1 and
        // 0, respectively.
        float32x4_t max_x4 = vdupq_n_f32(0.0f);
        float32x4_t ewma_x4 = vsetq_lane_f32(initial_value, vdupq_n_f32(0.0f), 3);
        int i;
        for (i = 0; i < last_index; i += 4) {
            ewma_x4 = vmulq_f32(ewma_x4, weight_prev_4th_x4);
            const float32x4_t sample_x4 = vld1q_f32(src + i);
            const float32x4_t sample_squared_x4 = vmulq_f32(sample_x4, sample_x4);
            max_x4 = vmaxq_f32(max_x4, sample_squared_x4);
            ewma_x4 = vmlaq_f32(ewma_x4, sample_squared_x4, smoothing_factor_x4);
        }

        // y[n] = z[n] + (1-a)^1(z[n-1]) + (1-a)^2(z[n-2]) + (1-a)^3(z[n-3])
        float ewma = vgetq_lane_f32(ewma_x4, 3);
        ewma_x4 = vmulq_f32(ewma_x4, weight_prev_x4);
        ewma += vgetq_lane_f32(ewma_x4, 2);
        ewma_x4 = vmulq_f32(ewma_x4, weight_prev_x4);
        ewma += vgetq_lane_f32(ewma_x4, 1);
        ewma_x4 = vmulq_f32(ewma_x4, weight_prev_x4);
        ewma += vgetq_lane_f32(ewma_x4, 0);

        // Fold the maximums together to get the overall maximum.
        float32x2_t max_x2 = vpmax_f32(vget_low_f32(max_x4), vget_high_f32(max_x4));
        max_x2 = vpmax_f32(max_x2, max_x2);

        std::pair<float, float> result(ewma, vget_lane_f32(max_x2, 0));

        // Handle remaining values at the end of |src|.
        for (; i < len; ++i) {
            result.first *= weight_prev;
            const float sample = src[i];
            const float sample_squared = sample * sample;
            result.first += sample_squared * smoothing_factor;
            result.second = std::max(result.second, sample_squared);
        }

        return result;
    }
#endif

} // namespace vector_math
} // namespace media
